

'''simple docstring'''






import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
 from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image






class 			lowerCAmelCase__     :




     """simple docstring"""





     def __init__(		self						:	Tuple      ,						A__						:	int      ,						A__						:	List[str]=1_3      ,						A__						:	int=6_4      ,						A__						:	Any=2      ,						A__						:	Tuple=3      ,						A__						:	int=True      ,						A__						:	Tuple=True      ,						A__						:	List[Any]=3_2      ,						A__						:	Union[str, Any]=5      ,						A__						:	Optional[int]=4      ,						A__						:	Optional[Any]=3_7      ,						A__						:	Union[str, Any]="gelu"      ,						A__						:	Dict=0.1      ,						A__						:	Optional[int]=0.1      ,						A__						:	Optional[Any]=1_0      ,						A__						:	Union[str, Any]=0.02      ,						A__						:	int=[1, 1_6, 4, 4]      ,						A__						:	Dict=None      ,						)       ->			Any:
          '''simple docstring'''




          a__   :      List[str]     =						parent
          a__   :      Optional[Any]     =						batch_size
          a__   :      List[Any]     =						image_size
          a__   :      Optional[Any]     =						patch_size
          a__   :      str     =						num_channels
          a__   :      List[Any]     =						is_training
          a__   :      Any     =						use_labels
          a__   :      List[str]     =						hidden_size
          a__   :      Any     =						num_hidden_layers
          a__   :      Optional[Any]     =						num_attention_heads
          a__   :      str     =						intermediate_size
          a__   :      Dict     =						hidden_act
          a__   :      List[str]     =						hidden_dropout_prob
          a__   :      Tuple     =						attention_probs_dropout_prob
          a__   :      List[Any]     =						type_sequence_label_size
          a__   :      Union[str, Any]     =						initializer_range
          a__   :      Optional[int]     =						scope
          a__   :      Tuple     =						backbone_featmap_shape

          # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          # the number of patches is based on the feature map of the backbone, which by default uses an output stride
          # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
          a__   :      List[str]     =						(self.image_size // 3_2) ** 2
          a__   :      List[str]     =						num_patches + 1







     def 			__lowerCAmelCase	(		self						:	Any	)       ->			List[Any]:
          '''simple docstring'''




          a__   :      Optional[int]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

          a__   :      str     =						None
          if self.use_labels:
               a__   :      Tuple     =						ids_tensor([self.batch_size]      ,						self.type_sequence_label_size	)

          a__   :      List[str]     =						self.get_config()

          return config, pixel_values, labels







     def 			__lowerCAmelCase	(		self						:	Optional[int]	)       ->			Optional[int]:
          '''simple docstring'''




          a__   :      Optional[int]     =						{
              '''global_padding''': '''same''',
              '''layer_type''': '''bottleneck''',
              '''depths''': [3, 4, 9],
              '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
              '''embedding_dynamic_padding''': True,
              '''hidden_sizes''': [4, 8, 1_6, 3_2],
              '''num_groups''': 2,
          }

          return ViTHybridConfig(
              image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						hidden_size=self.hidden_size      ,						num_hidden_layers=self.num_hidden_layers      ,						num_attention_heads=self.num_attention_heads      ,						intermediate_size=self.intermediate_size      ,						hidden_act=self.hidden_act      ,						hidden_dropout_prob=self.hidden_dropout_prob      ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,						is_decoder=lowerCAmelCase__      ,						initializer_range=self.initializer_range      ,						backbone_featmap_shape=self.backbone_featmap_shape      ,						backbone_config=lowerCAmelCase__      ,						)







     def 			__lowerCAmelCase	(		self						:	str      ,						A__						:	List[Any]      ,						A__						:	str      ,						A__						:	str	)       ->			Union[str, Any]:
          '''simple docstring'''




          a__   :      Union[str, Any]     =						ViTHybridModel(config=lowerCAmelCase__	)
          model.to(lowerCAmelCase__	)
          model.eval()
          a__   :      List[str]     =						model(lowerCAmelCase__	)
          self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)	)







     def 			__lowerCAmelCase	(		self						:	Dict      ,						A__						:	Tuple      ,						A__						:	int      ,						A__						:	Tuple	)       ->			List[str]:
          '''simple docstring'''




          a__   :      int     =						self.type_sequence_label_size
          a__   :      List[str]     =						ViTHybridForImageClassification(lowerCAmelCase__	)
          model.to(lowerCAmelCase__	)
          model.eval()
          a__   :      str     =						model(lowerCAmelCase__      ,						labels=lowerCAmelCase__	)
          self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size)	)







     def 			__lowerCAmelCase	(		self						:	Any	)       ->			Union[str, Any]:
          '''simple docstring'''




          a__   :      Dict     =						self.prepare_config_and_inputs()
          a__     ,    a__     ,    a__   :      List[str]     =						config_and_inputs
          a__   :      str     =						{'''pixel_values''': pixel_values}
          return config, inputs_dict






@require_torch
class 			lowerCAmelCase__     (					_UpperCAmelCase				,			_UpperCAmelCase				,			unittest.TestCase						):




     """simple docstring"""





     __UpperCamelCase									= (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
     __UpperCamelCase									= (
         {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
         if is_torch_available()
         else {}
     )
     __UpperCamelCase									= False
     __UpperCamelCase									= False
     __UpperCamelCase									= False







     def 			__lowerCAmelCase	(		self						:	Optional[int]	)       ->			Any:
          '''simple docstring'''




          a__   :      Any     =						ViTHybridModelTester(self	)
          a__   :      str     =						ConfigTester(self      ,						config_class=lowerCAmelCase__      ,						has_text_modality=lowerCAmelCase__      ,						hidden_size=3_7	)







     def 			__lowerCAmelCase	(		self						:	Any	)       ->			List[Any]:
          '''simple docstring'''




          self.config_tester.run_common_tests()







     @unittest.skip(reason='''ViT does not use inputs_embeds'''	)
     def 			__lowerCAmelCase	(		self						:	str	)       ->			List[Any]:
          '''simple docstring'''




          pass







     def 			__lowerCAmelCase	(		self						:	Tuple	)       ->			Optional[Any]:
          '''simple docstring'''




          a__     ,    a__   :      int     =						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a__   :      List[Any]     =						model_class(lowerCAmelCase__	)
               self.assertIsInstance(model.get_input_embeddings()      ,						(nn.Module)	)
               a__   :      Union[str, Any]     =						model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase__      ,						nn.Linear	)	)







     def 			__lowerCAmelCase	(		self						:	Dict	)       ->			List[str]:
          '''simple docstring'''




          a__     ,    a__   :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a__   :      Dict     =						model_class(lowerCAmelCase__	)
               a__   :      Tuple     =						inspect.signature(model.forward	)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               a__   :      Any     =						[*signature.parameters.keys()]

               a__   :      Optional[Any]     =						['''pixel_values''']
               self.assertListEqual(arg_names[:1]      ,						lowerCAmelCase__	)







     def 			__lowerCAmelCase	(		self						:	Union[str, Any]	)       ->			Optional[int]:
          '''simple docstring'''




          a__   :      Any     =						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCAmelCase__	)







     def 			__lowerCAmelCase	(		self						:	Optional[int]	)       ->			Union[str, Any]:
          '''simple docstring'''




          a__   :      Dict     =						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__	)







     def 			__lowerCAmelCase	(		self						:	List[str]	)       ->			int:
          '''simple docstring'''




          a__     ,    a__   :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

          a__   :      Dict     =						_config_zero_init(lowerCAmelCase__	)
          for model_class in self.all_model_classes:
               a__   :      str     =						model_class(config=lowerCAmelCase__	)
               # Skip the check for the backbone
               for name, module in model.named_modules():
                    if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                         a__   :      List[Any]     =						[F'{name}.{key}' for key in module.state_dict().keys()]
                         break

               for name, param in model.named_parameters():
                    if param.requires_grad:
                         if name in backbone_params:
                              continue
                         self.assertIn(
                             ((param.data.mean() * 1E9).round() / 1E9).item()      ,						[0.0, 1.0]      ,						msg=F'Parameter {name} of model {model_class} seems not properly initialized'      ,						)







     @slow
     def 			__lowerCAmelCase	(		self						:	Dict	)       ->			Tuple:
          '''simple docstring'''




          for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               a__   :      Optional[int]     =						ViTHybridModel.from_pretrained(lowerCAmelCase__	)
               self.assertIsNotNone(lowerCAmelCase__	)



def 			__a		(       ):
     a__   :      Tuple     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
     return image






@require_torch
@require_vision
class 			lowerCAmelCase__     (					unittest.TestCase						):




     """simple docstring"""





     @cached_property
     def 			__lowerCAmelCase	(		self						:	List[str]	)       ->			Dict:
          '''simple docstring'''




          return (
              ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
              if is_vision_available()
              else None
          )







     @slow
     def 			__lowerCAmelCase	(		self						:	Dict	)       ->			List[str]:
          '''simple docstring'''




          a__   :      List[str]     =						ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(
              lowerCAmelCase__	)

          a__   :      Dict     =						self.default_image_processor
          a__   :      Union[str, Any]     =						prepare_img()
          a__   :      int     =						image_processor(images=lowerCAmelCase__      ,						return_tensors='''pt'''	).to(lowerCAmelCase__	)

          # forward pass
          with torch.no_grad():
               a__   :      Optional[Any]     =						model(**lowerCAmelCase__	)

          # verify the logits
          a__   :      Tuple     =						torch.Size((1, 1_0_0_0)	)
          self.assertEqual(outputs.logits.shape      ,						lowerCAmelCase__	)

          a__   :      Optional[int]     =						torch.tensor([-1.9_090, -0.4_993, -0.2_389]	).to(lowerCAmelCase__	)

          self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,						lowerCAmelCase__      ,						atol=1E-4	)	)







     @slow
     @require_accelerate
     def 			__lowerCAmelCase	(		self						:	List[str]	)       ->			str:
          '''simple docstring'''




          a__   :      Optional[Any]     =						ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''	)
          a__   :      Optional[Any]     =						ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''      ,						device_map='''auto'''	)

          a__   :      int     =						prepare_img()

          a__   :      List[str]     =						image_processor(images=lowerCAmelCase__      ,						return_tensors='''pt'''	)
          a__   :      Tuple     =						model(**lowerCAmelCase__	)
          a__   :      List[Any]     =						outputs.logits
          # model predicts one of the 1000 ImageNet classes
          a__   :      Tuple     =						logits.argmax(-1	).item()

          self.assertTrue(model.config.idalabel[predicted_class_idx]      ,						'''tabby, tabby cat'''	)

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__a    :	Tuple         =    logging.get_logger(__name__)  # pylint: disable=invalid-name




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Optional[int]:

							'''simple docstring'''


							super().__init__()

							if safety_checker is None:
											logger.warning(
											    F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
											    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
											    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
											    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
											    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
											    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''		)

							self.register_modules(
							    speech_model=lowerCAmelCase__ ,  speech_processor=lowerCAmelCase__ ,  vae=lowerCAmelCase__ ,  text_encoder=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__ ,  feature_extractor=lowerCAmelCase__ ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ = "auto"		)	->   Dict:

							'''simple docstring'''


							if slice_size == "auto":
											__lowercase					     =				self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							self.enable_attention_slicing(lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=1_60_00 ,  lowerCAmelCase__ = 5_12 ,  lowerCAmelCase__ = 5_12 ,  lowerCAmelCase__ = 50 ,  lowerCAmelCase__ = 7.5 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 0.0 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = 1 ,  **lowerCAmelCase__ ,  )	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				self.speech_processor.feature_extractor(
							    lowerCAmelCase__ ,  return_tensors='''pt''' ,  sampling_rate=lowerCAmelCase__		).input_features.to(self.device		)
							__lowercase					     =				self.speech_model.generate(lowerCAmelCase__ ,  max_length=48_00_00		)

							__lowercase					     =				self.speech_processor.tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__ ,  normalize=lowerCAmelCase__		)[
							    0
							]

							if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				1
							elif isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				len(lowerCAmelCase__		)
							else:
											raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__		)}"		)

							if height % 8 != 0 or width % 8 != 0:
											raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."		)

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) or callback_steps <= 0)
							):
											raise ValueError(
											    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
											    F" {type(lowerCAmelCase__		)}."		)

							# get prompt text embeddings
							__lowercase					     =				self.tokenizer(
							    lowerCAmelCase__ ,  padding='''max_length''' ,  max_length=self.tokenizer.model_max_length ,  return_tensors='''pt''' ,  )
							__lowercase					     =				text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
											__lowercase					     =				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
											logger.warning(
											    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
											    F" {self.tokenizer.model_max_length} tokens: {removed_text}"		)
											__lowercase					     =				text_input_ids[:, : self.tokenizer.model_max_length]
							__lowercase					     =				self.text_encoder(text_input_ids.to(self.device		)		)[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							__lowercase     ,	__lowercase     ,	__lowercase					     =				text_embeddings.shape
							__lowercase					     =				text_embeddings.repeat(1 ,  lowerCAmelCase__ ,  1		)
							__lowercase					     =				text_embeddings.view(bs_embed * num_images_per_prompt ,  lowerCAmelCase__ ,  -1		)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							__lowercase					     =				guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
											__lowercase					     =				42
											if negative_prompt is None:
															__lowercase					     =				[''''''] * batch_size
											elif type(lowerCAmelCase__		) is not type(lowerCAmelCase__		):
															raise TypeError(
															    F"`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__		)} !="
															    F" {type(lowerCAmelCase__		)}."		)
											elif isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
															__lowercase					     =				[negative_prompt]
											elif batch_size != len(lowerCAmelCase__		):
															raise ValueError(
															    F"`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__		)}, but `prompt`:"
															    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
															    ''' the batch size of `prompt`.'''		)
											else:
															__lowercase					     =				negative_prompt

											__lowercase					     =				text_input_ids.shape[-1]
											__lowercase					     =				self.tokenizer(
											    lowerCAmelCase__ ,  padding='''max_length''' ,  max_length=lowerCAmelCase__ ,  truncation=lowerCAmelCase__ ,  return_tensors='''pt''' ,  )
											__lowercase					     =				self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
											__lowercase					     =				uncond_embeddings.shape[1]
											__lowercase					     =				uncond_embeddings.repeat(1 ,  lowerCAmelCase__ ,  1		)
											__lowercase					     =				uncond_embeddings.view(batch_size * num_images_per_prompt ,  lowerCAmelCase__ ,  -1		)

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											__lowercase					     =				torch.cat([uncond_embeddings, text_embeddings]		)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							__lowercase					     =				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							__lowercase					     =				text_embeddings.dtype
							if latents is None:
											if self.device.type == "mps":
															# randn does not exist on mps
															__lowercase					     =				torch.randn(lowerCAmelCase__ ,  generator=lowerCAmelCase__ ,  device='''cpu''' ,  dtype=lowerCAmelCase__		).to(
															    self.device		)
											else:
															__lowercase					     =				torch.randn(lowerCAmelCase__ ,  generator=lowerCAmelCase__ ,  device=self.device ,  dtype=lowerCAmelCase__		)
							else:
											if latents.shape != latents_shape:
															raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"		)
											__lowercase					     =				latents.to(self.device		)

							# set timesteps
							self.scheduler.set_timesteps(lowerCAmelCase__		)

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							__lowercase					     =				self.scheduler.timesteps.to(self.device		)

							# scale the initial noise by the standard deviation required by the scheduler
							__lowercase					     =				latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							__lowercase					     =				'''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
							__lowercase					     =				{}
							if accepts_eta:
											__lowercase					     =				eta

							for i, t in enumerate(self.progress_bar(lowerCAmelCase__		)		):
											# expand the latents if we are doing classifier free guidance
											__lowercase					     =				torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
											__lowercase					     =				self.scheduler.scale_model_input(lowerCAmelCase__ ,  lowerCAmelCase__		)

											# predict the noise residual
											__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__ ,  encoder_hidden_states=lowerCAmelCase__		).sample

											# perform guidance
											if do_classifier_free_guidance:
															__lowercase     ,	__lowercase					     =				noise_pred.chunk(2		)
															__lowercase					     =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											# compute the previous noisy sample x_t -> x_t-1
											__lowercase					     =				self.scheduler.step(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  **lowerCAmelCase__		).prev_sample

											# call the callback, if provided
											if callback is not None and i % callback_steps == 0:
															callback(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)

							__lowercase					     =				1 / 0.1_8215 * latents
							__lowercase					     =				self.vae.decode(lowerCAmelCase__		).sample

							__lowercase					     =				(image / 2 + 0.5).clamp(0 ,  1		)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							__lowercase					     =				image.cpu().permute(0 ,  2 ,  3 ,  1		).float().numpy()

							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return image

							return StableDiffusionPipelineOutput(images=lowerCAmelCase__ ,  nsfw_content_detected=lowerCAmelCase__		)